Gastos Casa
23 December, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 24/10/2024 | VTR | 21990 | Andrés | NA |
| 25/10/2024 | Comida | 8350 | Tami | Supermercado |
| 27/10/2024 | Comida | 78084 | Tami | Supermercado |
| 28/10/2024 | Diosi | 15000 | Andrés | NA |
| 29/10/2024 | Electricidad | 50227 | Andrés | NA |
| 31/10/2024 | Electrodomésticos/mantención casa | 13264 | Andrés | lona sombreadora |
| 31/10/2024 | Agua | 12000 | Andrés | NA |
| 4/11/2024 | Comida | 64915 | Tami | Supermercado |
| 7/11/2024 | Comida | 34229 | Andrés | piwen |
| 8/11/2024 | Assistcard viaje | 44836 | Tami | Assistcard viaje |
| 9/11/2024 | Comida | 35916 | Tami | Supermercado |
| 15/11/2024 | Comida | 12576 | Tami | Supermercado |
| 17/11/2024 | Comida | 91203 | Tami | Supermercado |
| 18/11/2024 | Diosi | 49500 | Tami | Veterinaria |
| 23/11/2024 | Comida | 50929 | Tami | Supermercado |
| 26/11/2024 | Enceres | 12990 | Tami | Confort 48 rollos |
| 27/11/2024 | Transporte | 21780 | Tami | Transvip aeropuerto |
| 27/11/2024 | Electricidad | 45000 | Andrés | Eléctrico |
| 30/11/2024 | Comida | 62899 | Tami | Supermercado |
| 11/12/2024 | Comida | 17738 | Andrés | compra lider despues viaje |
| 12/12/2024 | Comida | 55616 | Tami | Supermercado |
| 14/12/2024 | Diosi | 16276 | Andrés | antiparasitario |
| 15/12/2024 | Comida | 23773 | Tami | Supermercado |
| 18/12/2024 | Comida | 41554 | Tami | Supermercado |
| 18/12/2024 | VTR | 22000 | Andrés | NA |
| 20/12/2024 | Plata basureros | 10000 | Tami | NA |
| 3/12/2024 | Agua | 15828 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 22/12/2024 | Comida | 46608 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.5943e+08 2 4.7724 0.0087 **
## lag_depvar 2.6092e+11 1 2595.7309 <2e-16 ***
## Residuals 7.9711e+10 793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -2013.227 16470.90 0.1583777
## 2-0 31601.483 23271.144 39931.82 0.0000000
## 2-1 24372.645 19535.142 29210.15 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
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## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
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## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
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## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
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## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
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## 633 77359.43 2 88593.71
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## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
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## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
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## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
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## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 39523.43 2 50018.43
## 793 40397.14 2 39523.43
## 794 35446.71 2 40397.14
## 795 36987.14 2 35446.71
## 796 36526.29 2 36987.14
## 797 28969.29 2 36526.29
## 798 33366.43 2 28969.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 641 53835.75 22679.442
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 39523.43
## [792] 40397.14 35446.71 36987.14 36526.29 28969.29 33366.43
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2029.647781 4044.983416 -542.614443 2434.133563 -2978.614430
## 7 8 9 10 11
## 515.481090 -5660.648801 -1182.277616 -3960.028104 -406.066473
## 12 13 14 15 16
## -4929.500135 -1592.052718 -882.490567 393.317830 -3230.691902
## 17 18 19 20 21
## -362.036050 -2116.463737 6619.158571 -1529.765764 -1206.888865
## 22 23 24 25 26
## 1478.139947 -1188.218449 234.499241 1693.443283 -7107.608519
## 27 28 29 30 31
## 955.355518 8196.551921 405.410215 -26.819055 -2412.620720
## 32 33 34 35 36
## 1569.392416 4562.694906 1108.797697 2372.634168 -1889.819268
## 37 38 39 40 41
## 4591.584052 4294.118102 -2292.077908 -2991.453497 -1113.274166
## 42 43 44 45 46
## -10742.135964 7309.147865 2560.554176 1364.768812 8100.668170
## 47 48 49 50 51
## 666.708701 6510.403136 6685.857041 -5920.118093 -4817.286335
## 52 53 54 55 56
## -5069.724426 -7927.704816 6145.627159 -4074.564631 -4884.843550
## 57 58 59 60 61
## 3873.340700 895.696382 -27.036194 146.631203 -4992.945215
## 62 63 64 65 66
## 18139.274940 3616.917653 -3673.846679 5908.065897 7317.634409
## 67 68 69 70 71
## 14601.847581 1633.002315 -13268.465396 -1329.506958 4625.650532
## 72 73 74 75 76
## -4924.735382 -4416.473252 -10499.367072 2485.539932 -5388.025262
## 77 78 79 80 81
## 1084.557432 -6850.062646 574.443785 -2331.507039 -2669.076889
## 82 83 84 85 86
## -3904.918357 -506.256000 2343.031117 3783.001257 487.259423
## 87 88 89 90 91
## -476.539583 204.438470 4308.289006 -1165.927637 1150.475708
## 92 93 94 95 96
## -2067.117352 -1042.665330 180.966875 277.321722 -7482.428658
## 97 98 99 100 101
## 2407.772530 -8593.164431 -2915.464408 -4011.565701 -1704.199973
## 102 103 104 105 106
## -1229.284990 3211.619358 -2321.456475 2616.635148 -1143.755875
## 107 108 109 110 111
## 985.518098 2598.312077 -3149.717581 -4712.803287 -832.015177
## 112 113 114 115 116
## 1921.153348 11705.201374 -1255.441509 2659.701165 4249.871111
## 117 118 119 120 121
## 3482.984569 -1124.003197 -4735.166967 -3731.255737 2320.872139
## 122 123 124 125 126
## -1736.209373 1340.749562 8855.924857 827.706376 111.860059
## 127 128 129 130 131
## -2537.743290 2645.639731 7039.091311 986.880460 -8523.716655
## 132 133 134 135 136
## 1744.627168 4128.068555 -3178.593467 -1426.330067 -856.965507
## 137 138 139 140 141
## -3880.946629 1189.848763 -491.859282 -2909.478136 1727.484960
## 142 143 144 145 146
## -1876.330909 -7821.478749 2061.664997 -3464.357000 2122.429070
## 147 148 149 150 151
## -244.045960 1035.175637 -350.794281 1360.242042 1190.912477
## 152 153 154 155 156
## 3357.893688 -4867.326164 -1169.807215 -3229.480537 5968.562069
## 157 158 159 160 161
## 9745.200617 -3629.764149 -4972.224439 3416.324801 -4.161129
## 162 163 164 165 166
## 2494.010983 -6120.778453 -6947.291487 3967.763636 17190.439466
## 167 168 169 170 171
## 3379.515121 -653.608649 -2697.428397 -1347.565697 3350.931186
## 172 173 174 175 176
## -475.588136 -8320.658564 2638.762092 4092.440005 381.827563
## 177 178 179 180 181
## 8506.117168 -9512.937165 -3710.621232 -10974.960657 -11445.646271
## 182 183 184 185 186
## 1052.293461 9100.922819 -1650.675778 5708.963281 6317.657395
## 187 188 189 190 191
## 12901.802079 8139.123821 -4374.878710 2167.719326 10067.370662
## 192 193 194 195 196
## -1970.007205 -2760.723222 -10585.143439 -6635.792346 979.510716
## 197 198 199 200 201
## -5491.223856 -10039.421567 5166.036081 -3304.006259 -1941.376125
## 202 203 204 205 206
## -1030.861349 6266.878743 9631.493834 295.927265 2641.681536
## 207 208 209 210 211
## 2807.968432 5487.869583 12523.732310 -6029.591903 -11612.790789
## 212 213 214 215 216
## -5943.583628 -10845.557273 -5301.836504 1312.374380 -13233.256416
## 217 218 219 220 221
## 16201.644145 7565.754525 1258.995702 26414.970490 12172.041435
## 222 223 224 225 226
## 6951.343705 13633.082206 -4336.057384 -2134.368099 3403.712837
## 227 228 229 230 231
## -12.026687 2386.561951 8649.745703 5462.544590 -2275.012198
## 232 233 234 235 236
## -2176.351276 9094.590270 -11857.827576 -7588.086591 -8818.869765
## 237 238 239 240 241
## -10351.720297 2856.352326 1118.201973 -8537.083848 -9206.467705
## 242 243 244 245 246
## 8900.411466 -7993.594494 2279.250098 -10522.049965 -4247.925937
## 247 248 249 250 251
## 1233.747750 801.854981 -12526.842434 3464.150054 1862.749974
## 252 253 254 255 256
## 3998.353703 1902.250075 -1403.747360 10896.663116 20599.283773
## 257 258 259 260 261
## 2846.433695 -4625.582246 3776.835593 -2034.860281 3409.266457
## 262 263 264 265 266
## -5186.219462 -11205.801843 -5001.067141 -778.734597 -5445.383021
## 267 268 269 270 271
## 8535.951160 -4556.576283 3926.558047 -2386.588296 4157.567856
## 272 273 274 275 276
## 418.848449 7010.373867 -1730.310291 11714.252137 -4937.432152
## 277 278 279 280 281
## 1393.463528 -706.953302 7522.123409 -5412.033613 -3060.199215
## 282 283 284 285 286
## -11575.142001 -2936.490750 18396.936899 7451.837651 2376.564159
## 287 288 289 290 291
## -990.063745 554.117757 6049.045264 6514.169952 -19159.607724
## 292 293 294 295 296
## -11438.825978 -8371.711690 9447.447448 2811.121536 -1453.139817
## 297 298 299 300 301
## 27133.282173 9681.987730 4485.920462 9096.583369 2410.384631
## 302 303 304 305 306
## -1472.656315 7478.160172 -24731.388323 -3847.066035 -465.349741
## 307 308 309 310 311
## -7252.889585 -4221.857641 2700.578332 -9437.554501 -3433.862538
## 312 313 314 315 316
## -8378.310259 1406.132686 -3325.088057 1881.631818 -4266.773043
## 317 318 319 320 321
## 27272.302892 -1050.282677 2972.562092 10500.598699 5215.172795
## 322 323 324 325 326
## 31991.537811 4588.487967 -21453.085451 1401.525123 726.278648
## 327 328 329 330 331
## -6840.044740 -2065.753294 -33581.865615 766.845657 -2426.879106
## 332 333 334 335 336
## -211.801314 -3292.162754 3970.381163 -579.904291 -7098.510829
## 337 338 339 340 341
## -3233.367537 -2300.801742 -7786.359554 3773.792397 -1480.929201
## 342 343 344 345 346
## -1850.155421 -1106.686257 59.722179 354.991018 -1755.896857
## 347 348 349 350 351
## -9583.499909 -13307.724387 2266.605473 -4395.451488 -3722.860633
## 352 353 354 355 356
## -6040.446575 1705.292260 1312.267175 2658.180131 -3889.887800
## 357 358 359 360 361
## -631.581011 552.935755 6874.769587 91.052284 -230.074733
## 362 363 364 365 366
## 2387.462218 -2962.810915 -1075.705101 -8938.270233 -4773.802770
## 367 368 369 370 371
## -6339.891319 -5050.011667 -7335.915491 4959.158245 270.749602
## 372 373 374 375 376
## 7004.988561 -7801.570613 -2390.751688 -3510.944125 -2579.390040
## 377 378 379 380 381
## -12565.264899 1853.979944 -10709.956284 5665.171621 9253.801044
## 382 383 384 385 386
## 2976.036026 -2575.045700 1435.972622 6559.792647 11182.394343
## 387 388 389 390 391
## -6098.625341 -5625.227074 -389.934079 8329.911632 1530.690189
## 392 393 394 395 396
## 10929.551380 -10232.625075 2483.169255 408.719968 258.874648
## 397 398 399 400 401
## -955.814337 -856.573061 -14773.269745 8330.445662 -1421.389961
## 402 403 404 405 406
## -1602.689539 6761.719523 -8193.052405 -1505.065948 -2730.031628
## 407 408 409 410 411
## -6001.006593 -3005.776730 -4049.927066 -8869.706639 6063.808205
## 412 413 414 415 416
## 1520.885115 -7511.938219 -7793.485358 14155.548177 3647.274057
## 417 418 419 420 421
## 4290.785915 -8269.687232 -4928.564821 -2758.170924 2674.957672
## 422 423 424 425 426
## -14178.267600 -2877.708673 -9178.649115 2976.417414 6905.537567
## 427 428 429 430 431
## 6446.589485 -4166.078434 -4278.948336 -4860.120613 -1905.416692
## 432 433 434 435 436
## -5825.461225 -6714.953183 -6009.860941 -1432.517905 -895.511027
## 437 438 439 440 441
## -5033.923044 2537.295937 4762.641741 -5176.672406 -2257.262928
## 442 443 444 445 446
## 1479.209898 -3954.933886 2731.567916 -6709.476091 -12210.314174
## 447 448 449 450 451
## -4550.410284 9616.397740 -2134.285746 4654.422874 -6007.010963
## 452 453 454 455 456
## -1231.637745 272.947629 2904.844012 -12414.782855 3288.425109
## 457 458 459 460 461
## -6812.767844 6440.756872 2881.940057 2352.844595 -4018.459883
## 462 463 464 465 466
## 1940.130391 -175.963771 1621.984004 -704.695172 3168.996630
## 467 468 469 470 471
## -2840.913496 5620.257403 -7159.934533 -3137.507416 -2360.852929
## 472 473 474 475 476
## -4807.788180 2876.555317 7654.752232 -6207.810921 1329.447078
## 477 478 479 480 481
## -6343.890008 -2975.021675 1893.296666 -13065.516013 -9823.171305
## 482 483 484 485 486
## -1228.134423 -15.135426 -1013.042558 -1401.138533 -9649.487897
## 487 488 489 490 491
## 11070.675731 6132.728244 7274.657072 -5627.510099 5207.497700
## 492 493 494 495 496
## 9100.997584 5810.945355 -13743.995630 -10751.338202 -3565.927507
## 497 498 499 500 501
## -1215.815699 -634.135632 -7738.902793 534.856213 4198.889485
## 502 503 504 505 506
## 5388.013131 504.195375 -80.732271 -7401.579498 446.708931
## 507 508 509 510 511
## -5179.147997 1725.695161 -1419.567228 -8278.541099 -683.282709
## 512 513 514 515 516
## -2761.563080 -668.873432 1244.940012 -9598.453014 -7824.299598
## 517 518 519 520 521
## 24257.190930 9648.343475 5648.524255 -5593.384506 2577.991383
## 522 523 524 525 526
## 16787.777752 11154.062933 -24517.286531 -5275.847533 -3916.997067
## 527 528 529 530 531
## 4410.159867 -545.208773 -11287.988938 4268.214845 13757.146572
## 532 533 534 535 536
## -5208.290371 4174.474282 5333.254329 -2038.895285 -4772.642692
## 537 538 539 540 541
## -7274.933567 -2259.148730 8174.693962 -68.500038 -8335.423421
## 542 543 544 545 546
## 1669.190530 -758.568446 209.494693 -11190.972588 -11162.285267
## 547 548 549 550 551
## 1993.322741 6932.093946 -1437.722142 718.688543 -7848.990134
## 552 553 554 555 556
## 8474.384606 760.905277 -12097.932097 9071.341520 8507.094322
## 557 558 559 560 561
## -102.819990 4652.434891 -3800.050257 13906.689771 21220.812219
## 562 563 564 565 566
## -6854.059673 -10011.863861 6512.817990 -64.835985 3173.991934
## 567 568 569 570 571
## -7666.105127 -17543.424203 6531.690993 6265.152832 1698.960075
## 572 573 574 575 576
## 2889.169987 1549.059126 -2389.206289 14511.267055 -9931.627209
## 577 578 579 580 581
## -6463.621837 8531.397936 2632.177699 -6781.359137 7315.927996
## 582 583 584 585 586
## -4030.868273 -2979.013809 15518.531522 -14767.008647 8248.983480
## 587 588 589 590 591
## -152.974764 -6429.623075 -931.281215 81.329801 -10823.555795
## 592 593 594 595 596
## 1685.947598 -7269.440400 2978.213448 8753.462008 -7666.261914
## 597 598 599 600 601
## 5726.620181 2574.718027 6680.591383 -3400.893751 5961.306042
## 602 603 604 605 606
## -8516.534097 2088.397923 1092.334021 2956.312061 1299.425222
## 607 608 609 610 611
## 198.206145 -6006.818594 7914.408660 -1385.890316 -2764.059453
## 612 613 614 615 616
## -3624.596146 -8378.038922 11853.327721 4763.973982 -9510.033722
## 617 618 619 620 621
## 11485.276533 5847.593797 -5800.532663 26171.192099 -13143.443000
## 622 623 624 625 626
## -7009.485698 2973.491651 -4353.805343 -10758.915712 11187.493587
## 627 628 629 630 631
## -21805.189818 -2472.217019 8621.191601 11028.652733 -1718.993750
## 632 633 634 635 636
## 33128.779291 -6907.269207 5450.646228 5118.621730 -2559.818020
## 637 638 639 640 641
## -5607.377071 -2161.081935 -12632.586613 -2374.808042 -2007.855373
## 642 643 644 645 646
## -2634.059260 -2961.617495 1722.088655 4324.199081 16832.853097
## 647 648 649 650 651
## 18336.188602 582.504756 4500.980733 10316.221732 19820.505142
## 652 653 654 655 656
## 341.932732 -28436.315455 -1548.422800 -2480.235651 1700.210339
## 657 658 659 660 661
## -3361.048344 -10767.900896 1572.958114 4125.325764 -1128.060314
## 662 663 664 665 666
## 12916.917871 1186.001173 1640.037287 -11865.645874 1264.291394
## 667 668 669 670 671
## 1067.203062 -5289.674645 -7508.272285 2001.289396 -3789.968584
## 672 673 674 675 676
## 2608.696437 -3459.726758 -9405.500394 -8340.220113 -2987.629853
## 677 678 679 680 681
## 161.729485 2822.669340 666.329760 -3884.068621 -1856.563926
## 682 683 684 685 686
## -1366.495381 -8292.818567 4624.459934 -2296.141424 -1449.753084
## 687 688 689 690 691
## 534.519481 10791.443357 9738.063413 10473.644501 -9847.951285
## 692 693 694 695 696
## -3687.914688 -3254.511343 5770.980108 -10508.052032 -7989.373389
## 697 698 699 700 701
## -8660.297052 -6296.393660 -4747.258673 3080.149353 -4428.032080
## 702 703 704 705 706
## -1917.255199 4199.974747 31059.275596 9377.681627 23290.212643
## 707 708 709 710 711
## 1485.604385 8146.448112 22744.237449 6354.257581 -18397.758676
## 712 713 714 715 716
## 4694.719052 -5568.604203 -201.809223 386.452718 -17354.589434
## 717 718 719 720 721
## -5308.599683 3302.000222 -3055.106154 -13013.662071 4272.123479
## 722 723 724 725 726
## -5578.058263 729.970460 -3953.039423 -12460.349612 1378.000199
## 727 728 729 730 731
## -1866.884461 -9779.112815 17283.376412 1743.471375 -2760.326418
## 732 733 734 735 736
## 5682.652379 -8677.462005 -750.874340 8110.169953 -15399.955313
## 737 738 739 740 741
## -5924.934897 7403.255757 -4811.237278 142.223876 1805.188356
## 742 743 744 745 746
## -1985.514017 -5195.484226 6394.289459 -6311.141529 22675.254287
## 747 748 749 750 751
## 7751.072195 -2035.927323 -7367.059842 23358.102794 -4397.503057
## 752 753 754 755 756
## 1308.193833 -14506.927273 56060.874950 26760.678662 14900.900862
## 757 758 759 760 761
## -10846.098454 10453.092019 7157.403294 5655.957865 -46536.577134
## 762 763 764 765 766
## -16209.413801 961.784183 -2526.573146 -3463.881591 122842.379719
## 767 768 769 770 771
## 19082.503815 43485.156125 22301.297971 11786.751302 15571.671064
## 772 773 774 775 776
## 25456.896190 -99092.726578 -6830.245056 -35885.773906 1751.613535
## 777 778 779 780 781
## -1221.637145 3402.235390 -7417.684986 -1436.474273 -1937.398496
## 782 783 784 785 786
## 3432.650597 -7156.103269 -2226.761592 3930.218696 2266.007826
## 787 788 789 790 791
## -2764.151761 -4048.509836 1743.553012 2852.859228 -58.405378
## 792 793 794 795 796
## -10960.262243 -895.361126 -6610.960849 -735.104560 -2545.019997
## 797 798
## -9698.415985 1316.906852
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17239.64 20094.02 24358.76 24076.01 26435.33 23761.23 24479.36 19699.42
## 10 11 12 13 14 15 16 17
## 19435.31 16771.35 17550.79 14271.91 14323.20 14989.54 16690.41 15006.18
## 18 19 20 21 22 23 24 25
## 16043.46 15415.41 22515.77 21597.46 21076.00 22970.79 22295.07 22949.27
## 26 27 28 29 30 31 32 33
## 24799.89 18712.93 20443.45 28300.59 28358.39 28030.48 25653.89 27059.88
## 34 35 36 37 38 39 40 41
## 30912.63 31261.94 32674.68 30178.99 34148.88 37365.08 34413.74 31216.56
## 42 43 44 45 46 47 48 49
## 30061.42 20617.14 28154.87 30597.52 31689.47 38544.86 38038.17 42712.14
## 50 51 52 53 54 55 56 57
## 46959.12 39638.57 34193.30 29203.42 22330.52 28636.42 25208.41 21496.66
## 58 59 60 61 62 63 64 65
## 25916.16 27178.89 27476.65 27889.52 23750.01 40383.23 42231.85 37465.79
## 66 67 68 69 70 71 72 73
## 41683.37 46611.44 57306.57 55315.32 40521.22 38020.78 41046.31 35332.04
## 74 75 76 77 78 79 80 81
## 30772.80 21452.75 24662.31 20577.73 22669.06 17551.70 19572.22 18796.79
## 82 83 84 85 86 87 88 89
## 17822.06 15886.11 17167.11 20784.28 25213.17 26205.54 26230.56 26848.85
## 90 91 92 93 94 95 96 97
## 30984.36 29811.95 30813.83 28873.38 28071.18 28440.25 28847.86 22409.08
## 98 99 100 101 102 103 104 105
## 25431.74 18444.61 17297.85 15333.63 15634.14 16313.24 20797.17 19878.36
## 106 107 108 109 110 111 112 113
## 23398.33 23187.77 24868.12 27752.15 25243.95 21678.44 21954.56 24607.51
## 114 115 116 117 118 119 120 121
## 35499.44 33687.73 35529.84 38535.73 40496.57 38179.17 32987.11 29319.27
## 122 123 124 125 126 127 128 129
## 31407.35 29682.96 30867.50 38486.44 38128.00 37187.17 34042.79 35828.48
## 130 131 132 133 134 135 136 137
## 41239.98 40678.86 31858.37 33126.36 36324.16 32725.76 31108.97 30191.66
## 138 139 140 141 142 143 144 145
## 26740.01 28158.00 27927.05 25607.52 27637.05 26258.34 19844.34 22882.50
## 146 147 148 149 150 151 152 153
## 20703.71 23688.33 24229.68 25824.08 26006.62 27664.94 28968.96 32008.75
## 154 155 156 157 158 159 160 161
## 27467.52 26728.62 24277.72 30186.66 41650.19 39976.22 37334.53 42367.45
## 162 163 164 165 166 167 168 169
## 43779.56 47204.06 42658.58 37953.95 43392.85 59736.06 61953.75 60363.86
## 170 171 172 173 174 175 176 177
## 57181.57 55576.78 58286.16 57307.80 49580.52 52411.13 56163.17 56199.45
## 178 179 180 181 182 183 184 185
## 63346.22 53824.62 50567.39 41352.93 32870.99 36388.08 46516.96 45971.61
## 186 187 188 189 190 191 192 193
## 51939.34 57698.77 68508.88 73805.02 67483.85 67677.77 74765.86 70431.44
## 194 195 196 197 198 199 200 201
## 65943.00 55159.79 49174.92 50602.80 46186.42 38335.54 44776.43 42999.38
## 202 203 204 205 206 207 208 209
## 42636.43 43115.98 49927.08 58838.64 58467.32 60196.46 61856.42 65657.12
## 210 211 212 213 214 215 216 217
## 75147.45 67210.36 55369.73 49964.99 40938.69 37888.77 41010.26 31005.36
## 218 219 220 221 222 223 224 225
## 48021.53 55360.72 56264.89 79087.53 86601.37 88609.63 96220.06 87148.23
## 226 227 228 229 230 231 232 233
## 81131.57 80712.46 77354.01 76513.40 81262.31 82630.01 77051.49 72252.41
## 234 235 236 237 238 239 240 241
## 77920.26 64534.52 56551.01 48481.43 40071.93 44274.37 46432.51 39866.75
## 242 243 244 245 246 247 248 249
## 33530.45 43838.74 38071.18 42016.76 34261.21 32963.82 36628.29 39459.27
## 250 251 252 253 254 255 256 257
## 30265.71 36218.68 40029.65 45237.46 47962.60 47453.91 57780.72 75321.85
## 258 259 260 261 262 263 264 265
## 75136.44 68430.31 69915.86 66127.16 67576.93 61318.94 50566.64 46584.02
## 266 267 268 269 270 271 272 273
## 46793.95 42890.91 51717.15 47980.87 52138.02 50249.86 54327.44 54624.20
## 274 275 276 277 278 279 280 281
## 60656.74 58285.03 67982.29 61891.82 62102.38 60447.31 66204.61 59919.34
## 282 283 284 285 286 287 288 289
## 56474.57 46000.63 44393.35 61668.88 67212.86 67623.35 65034.45 64119.53
## 290 291 292 293 294 295 296 297
## 68130.54 72050.61 52999.40 43076.57 37072.55 47419.88 50669.85 49781.57
## 298 299 300 301 302 303 304 305
## 74038.73 79999.08 80668.42 85292.47 83486.51 78504.27 81979.82 56815.49
## 306 307 308 309 310 311 312 313
## 53067.21 52746.18 46520.71 43723.14 47335.55 39869.01 38587.88 33135.72
## 314 315 316 317 318 319 320 321
## 36929.80 36109.08 39950.20 37929.55 63780.85 61616.58 63244.26 71262.54
## 322 323 324 325 326 327 328 329
## 73655.89 99201.80 97575.37 73344.62 72139.44 70492.62 62424.04 59539.01
## 330 331 332 333 334 335 336 337
## 29411.58 33108.45 33549.09 35874.88 35214.05 40995.62 42073.94 37309.51
## 338 339 340 341 342 343 344 345
## 36521.94 36648.93 31956.06 37970.21 38635.30 38894.40 39772.42 41562.87
## 346 347 348 349 350 351 352 353
## 43389.47 43140.50 36067.30 26611.25 31969.45 30827.57 30416.59 28026.99
## 354 355 356 357 358 359 360 361
## 32717.73 36481.53 40956.46 39140.87 40404.35 42548.23 49962.23 50514.22
## 362 363 364 365 366 367 368 369
## 50716.39 53185.81 50662.85 50105.98 42732.52 39922.18 36089.44 33862.49
## 370 371 372 373 374 375 376 377
## 29910.27 37216.68 39509.44 47415.00 41371.32 40817.09 39350.68 38882.26
## 378 379 380 381 382 383 384 385
## 29726.73 34336.53 27370.54 35610.77 45970.11 49544.62 47813.60 49810.35
## 386 387 388 389 390 391 392 393
## 56046.32 65555.91 58749.94 53204.08 52932.09 60330.45 60855.16 69545.91
## 394 395 396 397 398 399 400 401
## 58623.83 60194.71 59753.70 59236.24 57719.29 56477.70 43202.55 51810.10
## 402 403 404 405 406 407 408 409
## 50807.98 49771.57 56189.20 48712.64 48022.03 46344.44 42010.63 40838.36
## 410 411 412 413 414 415 416 417
## 38897.28 32976.33 40869.26 43803.08 38461.77 33537.45 48447.15 52301.79
## 418 419 420 421 422 423 424 425
## 56241.12 48690.99 45004.89 43677.47 47273.12 35662.57 35391.08 29635.15
## 426 427 428 429 430 431 432 433
## 35239.32 43588.27 50498.08 47255.23 44316.41 41233.70 41121.60 37590.38
## 434 435 436 437 438 439 440 441
## 33718.86 30945.80 32525.94 34380.07 32379.56 37258.22 43479.67 40223.69
## 442 443 444 445 446 447 448 449
## 39928.93 42943.08 40823.72 44823.48 40058.17 31067.41 29901.89 41288.00
## 450 451 452 453 454 455 456 457
## 40968.72 46634.44 42259.35 42609.91 44234.58 47962.35 37810.57 42672.34
## 458 459 460 461 462 463 464 465
## 38083.81 45672.35 49201.44 51828.75 48549.87 50896.68 51098.73 52850.27
## 466 467 468 469 470 471 472 473
## 52346.57 55297.91 52619.31 57683.51 50926.08 48530.85 47113.36 43729.02
## 474 475 476 477 478 479 480 481
## 47494.82 54977.38 49389.98 51097.60 45873.02 44247.85 47088.09 36475.03
## 482 483 484 485 486 487 488 489
## 30019.99 31894.14 34597.76 36091.57 37059.92 30684.32 43246.84 49924.20
## 490 491 492 493 494 495 496 497
## 56772.08 51469.93 56315.43 63968.77 67790.00 54010.91 44564.50 42584.39
## 498 499 500 501 502 503 504 505
## 42908.42 43701.62 38174.14 40579.25 45894.42 51590.66 52302.16 52413.01
## 506 507 508 509 510 511 512 513
## 46098.72 47442.15 43691.73 46454.28 46119.11 39818.71 40952.71 40125.73
## 514 515 516 517 518 519 520 521
## 41234.20 43881.02 36702.73 31969.95 55921.09 64102.76 67765.10 61127.15
## 522 523 524 525 526 527 528 529
## 62470.08 76090.65 83085.29 57971.13 52828.00 49513.84 53904.07 53409.13
## 530 531 532 533 534 535 536 537
## 43567.50 48572.14 61265.15 55771.95 59178.32 63176.32 60221.36 55239.36
## 538 539 540 541 542 543 544 545
## 48684.86 47337.31 55294.79 55044.57 47585.52 49814.85 49641.08 50336.69
## 546 547 548 549 550 551 552 553
## 40961.71 32776.53 37129.48 45266.86 45063.31 46773.56 40768.04 49804.09
## 554 555 556 557 558 559 560 561
## 50962.36 40715.37 50280.76 58163.68 57526.99 61133.91 56890.31 68680.90
## 562 563 564 565 566 567 568 569
## 85412.20 75477.86 64012.18 68442.69 66562.29 67751.96 59300.42 43248.59
## 570 571 572 573 574 575 576 577
## 50275.13 56195.33 57381.12 59461.94 60110.63 57229.73 69507.63 58853.91
## 578 579 580 581 582 583 584 585
## 52560.89 60181.82 61689.64 54766.07 61048.58 56613.44 53650.47 67255.15
## 586 587 588 589 590 591 592 593
## 52646.59 60009.55 59099.62 52805.85 52109.24 52385.98 43078.20 45882.15
## 594 595 596 597 598 599 600 601
## 40494.93 44751.54 53537.12 46851.38 52725.28 55109.12 60792.61 56940.98
## 602 603 604 605 606 607 608 609
## 61766.96 53314.17 55198.95 55977.26 58291.29 58866.79 58406.39 52569.02
## 610 611 612 613 614 615 616 617
## 59648.60 57703.77 54793.60 51491.32 44436.39 55975.88 59873.18 50785.58
## 618 619 620 621 622 623 624 625
## 61213.98 65409.53 58882.81 81166.73 66251.77 58561.65 60569.66 55911.20
## 626 627 628 629 630 631 632 633
## 46222.08 56956.62 37463.65 37323.52 46916.06 57425.28 55464.93 84266.70
## 634 635 636 637 638 639 640 641
## 74428.07 76634.38 78275.82 72988.81 65689.65 62315.44 50189.81 48554.00
## 642 643 644 645 646 647 648 649
## 47442.77 45921.19 44301.77 46985.37 51614.43 66623.10 81083.78 78199.88
## 650 651 652 653 654 655 656 657
## 79105.92 84992.21 98470.78 93216.17 63411.28 60856.66 57803.36 58790.48
## 658 659 660 661 662 663 664 665
## 55222.47 45611.04 48001.39 52330.06 51520.22 63111.14 62988.53 63278.79
## 666 667 668 669 670 671 672 673
## 51705.14 53068.08 54089.10 49416.13 43380.71 46423.25 44016.02 47511.58
## 674 675 676 677 678 679 680 681
## 45258.36 38077.93 32722.49 32719.98 35475.90 40219.81 42485.93 40485.42
## 682 683 684 685 686 687 688 689
## 40509.07 40958.96 35287.11 41632.43 41128.61 41428.62 43429.13 54163.79
## 690 691 692 693 694 695 696 697
## 62642.36 70711.81 59981.77 55979.51 52854.02 58021.05 48289.52 41972.73
## 698 699 700 701 702 703 704 705
## 35853.11 32563.97 31040.14 36560.60 34819.83 35494.17 41442.01 70173.46
## 706 707 708 709 710 711 712 713
## 76347.50 93938.68 90248.69 92850.48 107913.31 106751.04 84056.14 84404.32
## 714 715 716 717 718 719 720 721
## 75720.95 72816.40 70787.88 53474.31 48861.14 52361.96 49860.52 38948.45
## 722 723 724 725 726 727 728 729
## 44530.34 40792.32 43043.04 40912.92 31597.00 35557.60 36184.40 29804.05
## 730 731 732 733 734 735 736 737
## 47916.81 50170.04 48199.06 53867.03 46254.73 46529.97 54531.24 40949.08
## 738 739 740 741 742 743 744 745
## 37352.17 45874.52 42641.06 44147.38 46922.94 46033.91 42444.14 49450.28
## 746 747 748 749 750 751 752 753
## 44459.03 65473.21 70806.64 66906.35 58821.75 78649.65 71706.81 70623.36
## 754 755 756 757 758 759 760 761
## 55824.13 104664.46 121777.10 126377.38 107857.77 110292.03 109537.61 107562.01
## 762 763 764 765 766 767 768 769
## 60123.27 45137.50 47051.43 45672.60 43644.19 152482.78 156930.56 182196.84
## 770 771 772 773 774 775 776 777
## 185772.11 179694.90 177687.39 184586.44 81551.82 72117.92 38410.10 41851.49
## 778 779 780 781 782 783 784 785
## 42261.48 46669.97 41055.05 41375.83 41218.06 45782.82 40507.19 40203.92
## 786 787 788 789 790 791 792 793
## 45330.42 48362.58 46612.80 43955.59 46701.00 50076.83 50483.69 41292.50
## 794 795 796 797 798
## 42057.68 37722.25 39071.31 38667.70 32049.52
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8127
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.772423 0.746764 3.744744
## t2* 2595.730929 169.085793 874.677686
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.024738 4.725208 12.6161
## 2 lag_depvar 1580.216055 2639.060891 4392.8265Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Dec 23 00:52:28 2024
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 6.190545 | 5.195333 | 5.410333 | 5.849167 | 6.677250 |
| Comida | 329.464273 | 366.009167 | 312.386750 | 317.896583 | 341.335167 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 88.688727 | 38.104750 | 47.072333 | 29.523000 | 43.320533 |
| Enceres | 26.169818 | 18.259750 | 24.219750 | 14.801167 | 24.056333 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.314383 |
| Gas/Bencina | 48.319273 | 42.636000 | 45.575000 | 13.583667 | 32.669133 |
| Diosi | 34.869000 | 55.804250 | 31.180667 | 52.687833 | 42.022733 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 17.992727 | 12.829167 | 25.156667 | 19.086917 | 18.902633 |
| Netflix | 1.517909 | 8.713833 | 7.151583 | 7.028750 | 6.506567 |
| Otros | 74.454545 | 5.481667 | 5.000000 | 0.000000 | 15.746333 |
| Total | 627.666818 | 563.738000 | 505.988083 | 474.453167 | 539.551067 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2557, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-12-31 00:04:58 sería de: 38.604 pesos// Percentil 95% más alto proyectado: 41.515,44
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 38416.82 | 38416.67 |
| Lo.80 | 38418.31 | 38418.00 |
| Point.Forecast | 38603.86 | 39152.75 |
| Hi.80 | 40264.77 | 44003.75 |
| Hi.95 | 41172.74 | 46571.71 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5610 -0.3022
## s.e. 0.1113 0.1114
##
## sigma^2 = 38132: log likelihood = -461.44
## AIC=928.88 AICc=929.25 BIC=935.58paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 xreg
## 0.3406 32.7856
## s.e. 0.1029 0.8890
##
## sigma^2 = 33275: log likelihood = -462.81
## AIC=931.63 AICc=931.99 BIC=938.37forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 969.7588 | 743.6487 | 776.0567 |
| Lo.80 | 1100.4934 | 888.3288 | 881.5743 |
| Point.Forecast | 1347.4570 | 1161.6360 | 1121.5821 |
| Hi.80 | 1594.4205 | 1455.6163 | 1446.9683 |
| Hi.95 | 1725.1551 | 1611.2400 | 1655.8181 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))